The corticospinal tract (CST) is an important descending motor pathway that coordinates hand and arm function in multiple species. However, the anatomy of this tract differs among species, as does the exact functional contribution as it relates to outcome measures currently used in the field. This pathway is disrupted following spinal cord injury (SCI), and efforts are underway in animal models to promote regeneration of the CST for recovery of function. However, there is a lack of standardized outcome measures in both humans and animal models (primates and rodents) that have been validated as translational common data elements to measure recovery of CST function. Additionally, efforts to replicate candidate therapies for clinical trials in the animal model have been unsuccessful, presumably due to claims about significant effects being made based on univariate analysis of only a few outcome measures of recovery. The appropriate outcome measures and animal models needed to validate preclinical studies have been extensively reported in the published literature, however there is a lack of consensus amongst researchers and clinicians as to which of these studies are necessary and sufficient when considering moving forward into clinical trials. We therefore hypothesize that by compiling a comprehensive database of raw data from published, preclinical studies of SCI pertaining to CST anatomy and functional recovery, using multivariate statistics, we will discover candidate common data elements that correlate to species-specific CST sprouting/regeneration. This will allow us to test additional data driven hypotheses, such as that the ratio of sprouting vs. spared CST axons is higher in monkeys than rats, and more predictive of multivariate functional recovery. Our goal with the proposed project is to validate the current outcome measures in the field that are sensitive to changes in CST anatomy, and to provide feedback to fine-tune the current and emerging measures that are appropriate for the animal model being used. This will foster an environment of highly efficient preclinical validation of emerging therapeutic strategies with a forward look towards clinical data to streamline our efforts to translate promising treatments from bench to bedside.